Many biological processes are RNA-mediated, but higher-order structures for most RNAs are unknown, making it difficult to understand how RNA structure governs function. Here we describe SHAPE mutational profiling (SHAPE-MaP) that makes possible de novo and large-scale identification of RNA functional motifs. Sites of 2’-hydroxyl acylation by SHAPE are encoded as non-complementary nucleotides during cDNA synthesis, as measured by massively parallel sequencing. SHAPE-MaP-guided modeling identified greater than 90% of accepted base pairs in complex RNAs of known structure and was used to define a second-generation model for the HIV-1 RNA genome. The HIV-1 model contains all known structured motifs and previously unknown elements, including experimentally validated pseudoknots. SHAPE-MaP yields accurate and high-resolution secondary structure models, enables analysis of low abundance RNAs, disentangles sequence polymorphisms in single experiments, and will ultimately democratize RNA structure analysis.

SHAPE chemistries exploit small electrophilic reagents that react with the 2′-hydroxyl group to interrogate RNA structure at single-nucleotide resolution. Mutational profiling (MaP) identifies modified residues based on the ability of reverse transcriptase to misread a SHAPE-modified nucleotide and then counting the resulting mutations by massively parallel sequencing. The SHAPE-MaP approach measures the structure of large and transcriptome-wide systems as accurately as for simple model RNAs. This protocol describes the experimental steps, implemented over three days, required to perform SHAPE probing and construct multiplexed SHAPE-MaP libraries suitable for deep sequencing. These steps include RNA folding and SHAPE structure probing, mutational profiling by reverse transcription, library construction, and sequencing. Automated processing of MaP sequencing data is accomplished using two software packages. ShapeMapper converts raw sequencing files into mutational profiles, creates SHAPE reactivity plots, and provides useful troubleshooting information, often within an hour. SuperFold uses these data to model RNA secondary structures, identify regions with well-defined structures, and visualize probable and alternative helices, often in under a day. We illustrate these algorithms with the E. coli thiamine pyrophosphate riboswitch, E. coli 16S rRNA, and HIV-1 genomic RNAs. SHAPE-MaP can be used to make nucleotide-resolution biophysical measurements of individual RNA motifs, rare components of complex RNA ensembles, and entire transcriptomes. The straightforward MaP strategy greatly expands the number, length, and complexity of analyzable RNA structures.

SUMMARY Messenger RNAs (mRNAs) can fold into complex structures that regulate gene expression. Resolving such structures de novo has remained challenging and has limited understanding of the prevalence and functions of mRNA structure. We use SHAPE-MaP experiments in living E. coli cells to derive quantitative, nucleotide-resolution structure models for 194 endogenous transcripts encompassing approximately 400 genes. Individual mRNAs have exceptionally diverse architectures, and most contain well-defined structures. Active translation destabilizes mRNA structure in cells. Nevertheless, mRNA structure remains similar between in-cell and cell-free environments, indicating broad potential for structure-mediated gene regulation. We find that translation efficiency of endogenous genes is regulated by unfolding kinetics of structures overlapping the ribosome binding site. We discover conserved structured elements in 35% of untranslated regions, several of which we validate as novel protein binding motifs. RNA structure regulates every gene studied here in a meaningful way, implying that most functional structures remain to be discovered.

Complex higher-order RNA structures play critical roles in all facets of gene expression; however, the through-space interaction networks that define tertiary structures and govern sampling of multiple conformations are poorly understood. Here we describe single-molecule RNA structure analysis in which multiple sites of chemical modification are identified in single RNA strands by massively parallel sequencing and then analyzed for correlated and clustered interactions. The strategy thus identifies RNA interaction groups by mutational profiling (RING-MaP) and makes possible two expansive applications. First, we identify throughspace interactions, create 3D models for RNAs spanning 80-265 nucleotides, and characterize broad classes of intramolecular interactions that stabilize RNA. Second, we distinguish distinct conformations in solution ensembles and reveal previously undetected hidden states and large-scale structural reconfigurations that occur in unfolded RNAs relative to native states. RING-MaP single-molecule nucleic acid structure interrogation enables concise and facile analysis of the global architectures and multiple conformations that govern function in RNA. These functions are mediated by tiered levels of information: The simplest is the primary sequence, and the most complex is the higher-order structure that governs interactions with ligands, proteins, and other RNAs (1, 2). Many RNAs can form more than one stable structure, and these distinct conformations often have different biological activities (3, 4). Currently, the rate of describing new RNA sequences vastly exceeds abilities to examine their structures.Here we characterize through-space interactions and multiple conformations in single RNAs by melding chemical probing and massively parallel sequencing. Because massively parallel sequencing reports the sequences of single templates, each read is fundamentally a single-molecule observation (5). We first modified RNA with a reagent that is sensitive to the underlying RNA structure and then detected multiple adducts in individual RNA strands (Fig. 1). Chemical adducts were detected as sequence mutations based on their ability to induce efficient misreading of the template nucleotide by a reverse transcriptase enzyme, an approach called mutational profiling, or MaP (6). Singlemolecule probing data were used in two distinct ways: to detect correlated RNA modifications reflecting higher-order through-space interactions (Fig. 1A) and to examine multiple conformations in single in-solution ensembles (Fig. 1B). Results and DiscussionMultisite Dimethyl Sulfate Reactivity with RNA. We used dimethyl sulfate (DMS) to probe the structures of three RNAs: the Escherichia coli thiamine pyrophosphate (TPP) riboswitch (79 nt) (7), the Tetrahymena group I intron P546 domain (160 nt) (8), and the Bacillus stearothermophilus RNase P catalytic domain (265 nt) (9). RNAs were selected to illustrate distinct RNA folding features and to emphasize increasingly difficult analysis challenges. The TPP riboswitch binds the...

Mutational profiling (MaP) enables detection of sites of chemical modification in RNA as sequence changes during reverse transcription (RT), subsequently read out by massively parallel sequencing. We introduce ShapeMapper 2, which integrates careful handling of all classes of adduct-induced sequence changes, sequence variant correction, basecall quality filters, and quality-control warnings to now identify RNA adduct sites as accurately as achieved by careful manual analysis of electrophoresis data, the prior highest-accuracy standard. MaP and ShapeMapper 2 provide a robust, experimentally concise, and accurate approach for reading out nucleic acid chemical probing experiments.